Potential caveats to explanation of test exposure factor

[kdpenner]

I appreciate that y'all wrote a tutorial with the basic epidemiology needed to estimate R(t). I was with the explanation until this:

Intuitively, if we test twice as much, we expect twice as many positive tests to show up.

This is not intuitive to me. My intuition is that most tests have been done on a biased sample of the symptomatic and the exposed, for example those who know they've been exposed because they were near a symptomatic person. The bias implies that testing twice the sample does not necessarily lead to twice as many positives. Throw in the possibility of asymptomatic cases and different transmissibility of the asymptomatic, and I'm no longer sure what an unbiased sample is. Is it a random subset of the population? Probably not.

If these are significant caveats in mapping model positives to data positives they should be mentioned.